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Photovoltaic (PV) power generation and the types of connected loads both have an effect on protective impedance relays’ readings. This paper investigates this effect in a real distribution system installed in the State of Kuwait. It is found that, both the dynamic loads and the PV plants have considerable effects in the relay impedance value which vary according to the load type, PV connection and fault place. If the fault occurs at the location of the PV system’s bus when no PV power is generated, the dynamic load causes the relay impedance to increase, while connecting the PV decreases the relay impedance. The relay’s resistance and reactance increase from 0.3153Ω and 1.4950Ω, to 0.3456Ω and 1.6617Ω respectively when the dynamic load increases from 25% to 90% of the total load at constant high fault resistance. The relay resistance and reactance decrease from 0.2849Ω and 0.3443Ω (without PV plant), to 0.2195Ω and 0.3137Ω (with PV), respectively. When the dynamic load percentage increases from 25% to 90%, the resistance and reactance of the relay decrease from 1.0488Ω and 0.0051Ω, to 0.9526Ω and 0.0008Ω, respectively. This phenomenon is valid for all expected fault resistances. When considering constant dynamic load percentage and constant fault resistance, the relay resistance and reactance increase from 1.375Ω and 0.0022Ω (without PV) to 1.5745Ω and 0.0726 Ω (with PV), respectively. Based on those results, the impedance relay setting must be adjusted according to the percentage of the dynamic loads percentage, the PV penetration level, and the fault location. [ABSTRACT FROM AUTHOR]

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. [ABSTRACT FROM PUBLISHER]

Abstract: The need of reducing CO2 emissions in electricity generation field for solving global warming problems has led to increase interest in Micro-Grid (MG) especially the one with renewable sources such as solar and wind generations. Wind speed fluctuations cause high fluctuations in output power of wind turbine which cause fluctuations in fr and voltages of the MG in the islanding mode and originate stability problems. In this study, a new fuzzy logic pitch controller and an energy storage ultra capacitor are proposed and developed to smooth the output power of wind turbine and enhance MG''s performance in islanding mode. These two proposed controllers are compared with the conventional PI pitch controller, which is usually used to control wind generation system when the wind speed exceeds a rated value. Obtained results proved that our two proposed strategies are effective for the MG performance improvement during islanding mode. All models and controllers are developed using Matlab® Simulink® environment. [Copyright &y& Elsevier]

Power deregulation and shortage of transmission capacities have led to increased interest in distributed generators, especially renewable sources. In this study, a complete model is developed that can simulate in detail the transient dynamic performance of the micro-grid during and subsequent to the islanding process. Wind speed fluctuations cause high fluctuations in output power of a wind turbine, resulting in fluctuations in frequency and voltages of the micro-grid during islanding mode. In this study, new proportional integral pitch controller is proposed to smooth the output power of a wind turbine to reduce frequency and voltage fluctuations. The proposed proportional integral controller is compared with the conventional proportional integral controller that is used for wind turbine power control. The obtained results proved that the proposed controller is effective for the improvement of micro-grid performances. In addition, a neuro-fuzzy controller is also proposed for obtaining maximum power point tracking of the photovoltaic panels installed in the micro-grid. All models and controllers are developed on a Matlab® Simulink® environment. [ABSTRACT FROM AUTHOR]